Federated Learning [1]}, [2]}, [3]}, [4]}, [5]}, is a method for updating a centralized model using a training set that is distributed among multiple users. Federation allows for private local data by collecting limited updates to a base model and aggregating them in a centralized location. After a centralized model has been updated, it is shared back with agents. Building on Federated Learning, Federated Multi-Task Learning [6]}, [7]}, [8]} considers the known shared structure between pairs of models, improving the effectiveness of samples extracted from each of the local models. A federated averaging method for mitigating the overhead required for decentralized updates to a centralized model over a network [9]} is suggested. Here, in each iteration, a random set of local models is selected to run a single step of gradient descent using local data and transmit back the results, which are then averaged. Federated learning captures the real-time model construction we are interested in but is applied towards building a centralized model aggregated from distributed (typically i.i.d) data, thus losing the local variant that we are after.
